Comments in Python 3

Functions in Python are blocks of reusable code that you can access by calling the function name and passing arguments. Using functions in Python significantly simplifies a programmer's work because, instead of writing code repeatedly, one can simply call a function. How to Create a Function in Pyhton Let's start with an example and then move on to the explanation: def multiply(first, second):    return first * second We have just written a function that performs a simple task: it multiplies the values (arguments) passed to it. These values can then be entered after the function name in the program to get the product of the factors. Now, enter the following in IDLE: >>> multiply(7, 8) Arguments can include not only whole numbers but also decimal numbers, for example: >>> multiply(7.4, 8.2)60.68 Now, let's break down the code. Here, we define a Python function using the def keyword and the function name. In parentheses, we specify parameters that will accept various arguments from user input. A colon must follow the closing parenthesis, after which a new line with indentation starts the function body, describing what the function does. If you're writing code in an editor, the indentation will be added automatically. We used the return operator, which explicitly returns arguments. Note that after return, there is an instruction on what the program should do with the arguments. In this case, it multiplies them. Practical Example of Using Python Functions Here, we will demonstrate how Python functions help optimize routine tasks. The following example is simplified but illustrative. By understanding how functions work, you can learn to solve your own tasks, which will become more complex and interesting as you progress in the language. Let's say we opened a bookstore and purchased a cash register, and the cashier had already issued receipts for the first customers. Initially, a receipt might look like this: print("Learn Now, LLC") print("Programming Book", end=" ") print(1, end=" pcs. ") print(50, end=" euro") print("\nAdvanced Programming Book", end=" ") print(1, end=" pcs. ") print(100, end=" euro") print("\nTotal:", 150, end=" euro") print("\nThank you for your purchase!") Output: Learn Now, LLC Programming Book 1 pcs. 50 euro Advanced Programming Book 1 pcs. 100 euro Total: 150 euro Thank you for your purchase! Now, imagine that a whole stack of books has been purchased, and the number of customers is increasing daily. While you manually calculate the total for one customer, others start getting impatient. This is where automation comes in.  Let's say someone buys seven different books, with some books purchased in multiple copies: def check(book_attr): total = 0 print("Learn Now, LLC") for book in book_attr: a = book[0] b = book[1] c = book[2] print(f"{a} ({b} pcs.) - {c} euro") total += b * c print(f"\nTotal: {total} euro") print("Thank you for your purchase!") book_attr = [ ("Programming Book", 2, 50), ("Advanced Programming Book", 2, 100), ("Programming Book 80 lvl", 2, 195), ("Beginner's Guide to Python", 1, 120), ("You Can Become a Programmer", 1, 98), ("Functional Programming in Python", 1, 95), ("Secrets of Clean Code", 1, 80), ] As we can see, new variables appeared, and the purchase list was placed in a separate block. Now, when generating a new receipt, all we need to do for automatic total calculation is enter the book names, quantities, and prices per unit. Once all items are entered, we call our function with the parameter formatted as a tuple above: check(book_attr) This produces the following output: Learn Now, LLC Programming Book (2 pcs.) - 50 euro Advanced Programming Book (2 pcs.) - 100 euro Programming Book 80 lvl (2 pcs.) - 195 euro Beginner's Guide to Python (1 pcs.) - 120 euro You Can Become a Programmer (1 pcs.) - 98 euro Functional Programming in Python (1 pcs.) - 95 euro Secrets of Clean Code (1 pcs.) - 80 euro Total: 1083 euro Thank you for your purchase! That's it! The total amount was calculated automatically. Let’s break down the code: The variable total stores the purchase total and changes as new values are added to the tuple. A for loop is used to define a set of variables that store the following values: a: product name b: quantity c: price per unit Next, we give the print command. The letter f in print statements (which is itself a built-in function, by the way) means that f-strings are used. For now, it's enough to know that they are a convenient formatting method, and the code is self-explanatory. The next line should not be surprising: it calculates the total by multiplying the quantity of each item by its price and adding the result to the running total. Finally, we use another f-string for text formatting, and we have already discussed the tuple block that stores the necessary data for purchase calculations. Features of Functions in Python Key Advantages: No need to repeat specific blocks of code, which can sometimes be quite large. Functions can be called as many times as needed, even consecutively. When divided into multiple functional blocks, large programs become much easier to track. There are almost no downsides to functions in Python, except that they may not always be convenient. In some cases, it is easier to use generators, as certain functions (e.g., filter) may return iterators, requiring additional code to process them. For example, if we enter the following in IDLE: >>> numbers = [2, 4, 6, 8, 10, 12, 14] >>> filter(lambda num: num >= 10, numbers) We get this result: <filter object at 0x00000000030C3220> To correctly display elements that meet the condition, we need to wrap this expression as follows: >>> list(filter(lambda num: num >= 10, numbers))[10, 12, 14] Built-in Functions in Python You have almost certainly used them in your first Python lesson. Here’s an example: print("Hello, World!") The print function is a built-in function, and "Hello, World!" is its argument. Python has hundreds, even thousands, of built-in functions, especially when additional libraries are included. You don't need to know all of them; you can always check the documentation if you encounter an unfamiliar function. However, you will need to learn some common built-in functions, as these core elements are essential for writing any useful program. Here are some commonly used built-in functions: len returns the length (number of elements) of a sequence such as a string, list, tuple, range, or array: flowers = ["bellflower", "cornflower", "buttercup", "forget-me-not", "daisy"]len(flowers) Output: 5 str converts numbers into strings (since Python does not allow direct concatenation of strings and numbers): year = 2008"Euro " + str(year) Output: 'Euro 2008' int converts strings into integers. It also rounds floating-point numbers to the nearest integer, always towards zero: int(554.995) Output: 554 float converts integer values into floating-point numbers, which can be useful for certain calculations: float(55) Output: 55.0 tuple converts lists into tuples: flowers = ["bellflower", "cornflower", "buttercup", "forget-me-not", "daisy"]tuple(flowers) Output: ('bellflower', 'cornflower', 'buttercup', 'forget-me-not', 'daisy') dict allows you to create dictionaries. Here’s an example of creating a dictionary from a list of tuples using dict: clubs = [('Barcelona', 1), ('Juventus', 3), ('Liverpool', 2), ('Real Madrid', 5), ('Bayern München', 4)]dict(clubs) Output: {'Barcelona': 1, 'Juventus': 3, 'Liverpool': 2, 'Real Madrid': 5, 'Bayern München': 4} range creates number sequences, which can be useful for iterating through numeric values: for number in range(0, 30, 3): print(number) Output: 0 3 6 9 12 15 18 21 24 27 The range function takes three parameters: The first two define the range limits. The third (optional) parameter specifies the step. In this case, numbers from 0 to 30 are printed in steps of 3. The upper bound is not included in the output. To include it, the range should be extended slightly: for number in range(0, 31, 3): print(number) Output: 0 3 … 27 30 Using the Result of One Function in Another Python Function Finally, let’s look at another interesting technique. Since functions in Python are objects, they can be passed as arguments to other functions and referenced. def check(company="Learn Now"): """Allows inserting different company names in the receipt""" print(f"{company}, LLC") Let’s enter the name of another company: check("Enlightenment") Output: Enlightenment, LLC Now, let’s pass the created function to the built-in help function to learn what it does: help(check) Output: Help on function check in module __main__: check(company='Learn Now') Allows inserting different company names in the receipt As we can see, it is quite simple. What We Learned In this tutorial, we explored how functions work in Python 3 and learned how to create and use them. We discussed built-in tools and examined an example of passing functions as objects to other functions. By studying functions more deeply, you will appreciate their usefulness even when writing relatively small applications.

PyTorch is a free, open-source deep learning library. With its help, a computer can detect objects, classify images, generate text, and perform other complex tasks. PyTorch is also a rich tool ecosystem that supports and accelerates AI development and research. In this article, we will cover only the basics: we will learn how to install PyTorch and verify that it works. To work with PyTorch, you will need: At least 1 GB of RAM. Installed Python 3 and pip.  A configured local development environment. Deep knowledge of machine learning is not required for this tutorial. It is assumed that you are familiar with basic Python terms and concepts. Installing PyTorch We will be working in a Windows environment but using the command line. This makes the tutorial almost universal—you can use the same commands on Linux and macOS. First, create a workspace where you will work with Torch Python. Navigate to the directory where you want to place the new folder and create it: mkdir pytorch Inside the pytorch directory, create a new virtual environment. This is necessary to isolate projects and, if needed, use different library versions. python3 -m venv virtualpytorch To activate the virtual environment, first go to the newly created directory: cd virtualpytorch Inside, there is a scripts folder (on Windows) or bin (on other OS). Navigate to it: cd scripts Activate the virtual environment using a bat file by running the following command in the terminal: activate.bat The workspace is now ready. The next step is to install the PyTorch library. The easiest way to find the installation command is to check the official website. There is a convenient form where you select the required parameters. As an example, install the stable version for Windows using CPU via pip. Select these parameters in the form, and you will get the necessary command: pip3 install torch torchvision torchaudio Copy and execute the pip install torch command in the Windows command line. You are also installing two sub-libraries: torchvision – contains popular datasets, model architectures, and image transformations for computer vision. torchaudio – a library for processing audio and signals using PyTorch, providing input/output functions, signal processing, datasets, model implementations, and application components. This is the standard setup often used when first exploring the library. The method described above is not the only way to install PyTorch. If Anaconda is installed on Windows, you can use its graphical interface. If your computer has NVIDIA GPUs, you can select the CUDA version instead of CPU. In that case, the installation command will be different. All possible local installation methods are listed in the official documentation. You can also find commands for installing older versions of the library there. To install them, just select the required version and install it the same way as the current package builds. You don't need to write a script to check if the library is working. The Python interpreter has enough capabilities to perform basic operations. If you have successfully installed PyTorch in the previous steps, then launching the Python interpreter won’t be an issue. Run the following command in the command line: python Then enter the following code: import torch x = torch.rand(5, 3) print(x) You should see an output similar to this: tensor([[0.0925, 0.3696, 0.4949], [0.0240, 0.2642, 0.1545], [0.7274, 0.4975, 0.0753], [0.4438, 0.9685, 0.5022], [0.4757, 0.6715, 0.4298]]) Now, you can move on to solving more complex tasks. PyTorch Usage Example To make learning basic concepts more engaging, let’s do it in practice. For example, let’s create a neural network using PyTorch that can recognize the digit shown in an image. Prerequisites To create a neural network, we need to import eight modules: import torch import torchvision import torch.nn.functional as F import matplotlib.pyplot as plt import torch.nn as nn import torch.optim as optim from torchvision import transforms, datasets All of these are standard PyTorch libraries plus Matplotlib. They handle image processing, optimization, neural network construction, and graph visualization. Loading and Transforming Data We will train the neural network on the MNIST dataset, which contains 70,000 images of handwritten digits. 60,000 images will be used for training. 10,000 images will be used for testing. Each image is 28 × 28 pixels. Each image has a label representing the digit (e.g., 1, 2, 5, etc.). train = datasets.MNIST("", train=True, download=True, transform=transforms.Compose([transforms.ToTensor()])) test = datasets.MNIST("", train=False, download=True, transform=transforms.Compose([transforms.ToTensor()])) trainset = torch.utils.data.DataLoader(train, batch_size=15, shuffle=True) testset = torch.utils.data.DataLoader(test, batch_size=15, shuffle=True) First, we divide the data into training and testing sets by setting train=True/False. The test set must contain data that the machine has not seen before. Otherwise, the neural network’s performance would be biased. Setting shuffle=True helps reduce bias and overfitting. Imagine that the dataset contains many consecutive "1"s. If the machine gets too good at recognizing only the digit 1, it might struggle to recognize other numbers. Shuffling the data prevents the model from overfitting specific patterns and ensures a more generalized learning process. Definition and Initialization of the Neural Network The next step is defining the neural network: class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 86) self.fc2 = nn.Linear(86, 86) self.fc3 = nn.Linear(86, 86) self.fc4 = nn.Linear(86, 10) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.fc4(x) return F.log_softmax(x, dim=1) model = NeuralNetwork() The neural network consists of four layers: one input layer, two hidden layers, and one output layer. The Linear type represents a simple neural network. For each layer, it is necessary to specify the number of inputs and outputs. The output number of one layer becomes the input for the next layer. The input layer has 784 nodes. This is the result of multiplying 28 × 28 (the image size in pixels). The first hidden layer has 86 output nodes, so the input to the next layer must be 86 as well.The same logic applies further. 86 is an arbitrary number—you can use a different value. The output layer contains 10 nodes because the images represent digits from 0 to 9. Each time data passes through a layer, it is processed by an activation function. There are several activation functions. In this example, we use ReLU (Rectified Linear Unit). This function returns 0 if the value is negative or the value itself if it is positive. The softmax function is used at the output layer to normalize values. For example, it might return an 80% probability that the digit in the image is 1, or a 30% probability that the digit is 5, and so on. The highest probability is selected as the final prediction. Training The next step is training. optimizer = optim.Adam(model.parameters(), lr=0.001) EPOCHS = 3 for epoch in range(EPOCHS): for data in trainset: X, y = data model.zero_grad() output = model(X.view(-1, 28 * 28)) loss = F.nll_loss(output, y) loss.backward() optimizer.step() print(loss) The optimizer calculates the difference (loss) between the actual data and the prediction, adjusts the weights, recalculates the loss, and continues the cycle until the loss is minimized. Training Verification Here, we compare the actual values with the predictions made by the model. For this tutorial, the accuracy is high because the neural network effectively recognizes each digit. correct = 0 total = 0 with torch.no_grad(): for data in testset: data_input, target = data output = model(data_input.view(-1, 784)) for idx, i in enumerate(output): if torch.argmax(i) == target[idx]: correct += 1 total += 1 print('Accuracy: %d %%' % (100 * correct / total)) To verify that the neural network works, pass it an image of a digit from the test set: plt.imshow(X[1].view(28,28)) plt.show() print(torch.argmax(model(X[1].view(-1, 784))[0])) The output should display the digit shown in the provided image. Final Script Here’s the full script you can run to see how the neural network works: import torch import torchvision import torch.nn.functional as F import matplotlib.pyplot as plt import torch.nn as nn import torch.optim as optim from torchvision import transforms, datasets train = datasets.MNIST("", train=True, download=True, transform = transforms.Compose([transforms.ToTensor()])) test = datasets.MNIST("", train=False, download=True, transform = transforms.Compose([transforms.ToTensor()])) trainset = torch.utils.data.DataLoader(train, batch_size=15, shuffle=True) testset = torch.utils.data.DataLoader(test, batch_size=15, shuffle=True) class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 86) self.fc2 = nn.Linear(86, 86) self.fc3 = nn.Linear(86, 86) self.fc4 = nn.Linear(86, 10) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.fc4(x) return F.log_softmax(x, dim=1) model = NeuralNetwork() optimizer = optim.Adam(model.parameters(), lr=0.001) EPOCHS = 3 for epoch in range(EPOCHS): for data in trainset: X, y = data model.zero_grad() output = model(X.view(-1, 28 * 28)) loss = F.nll_loss(output, y) loss.backward() optimizer.step() print(loss) correct = 0 total = 0 with torch.no_grad(): for data in testset: data_input, target = data output = model(data_input.view(-1, 784)) for idx, i in enumerate(output): if torch.argmax(i) == target[idx]: correct += 1 total += 1 print('Accuracy: %d %%' % (100 * correct / total)) plt.imshow(X[1].view(28,28)) plt.show() print(torch.argmax(model(X[1].view(-1, 784))[0])) Each time we run the network, it will take a random image from the test set and analyze the digit depicted on it. After the process is completed, it will display the recognition accuracy in percentage, the image itself, and the digit recognized by the neural network. This is how it looks: Conclusion PyTorch is a powerful open-source machine learning platform that accelerates the transition from research prototypes to production deployments. With it, you can solve various tasks in the fields of artificial intelligence and neural networks. You don’t need deep knowledge of machine learning to begin working with PyTorch. It is enough to know the basic concepts to repeat and even modify popular procedures like image recognition to suit your needs. A big advantage of PyTorch is the large user community that writes tutorials and shares examples of using the library. Object recognition in images is one of the simplest and most popular tasks in PyTorch for beginners. However, the capabilities of the library are not limited to this. To create powerful neural networks, you need a lot of training data. These can be stored, for example, in an object-based S3 storage such as Hostman, with instant data access via API or web interface. This is an excellent solution for storing large volumes of information.

This article will teach you how to create a Python virtual environment. It is useful for Python developers to avoid issues with different versions of libraries. A simple example: You have two applications that rely on the same library, but each requires a different version. Another example: You want to ensure that your application runs independently of library updates installed in the global Python storage. A third example: You do not have access to this global storage. The solution in all three cases is to create a Python virtual environment.  The module name venv is short for Virtual Environment. It is a great tool for project isolation, functioning like a sandbox. It allows you to run an application with its dependencies without interfering with other applications that use different versions of the same software. As a result, each application runs in its own virtual environment, isolated from others, increasing the overall stability of all applications. How to Create a Virtual Environment in Python 3 Good news: You don’t need to install venv separately on Windows—it is part of the standard Python 3 library and comes with the interpreter. On Linux, however, venv is not always included in the OS package, so you might need to install it. On Ubuntu/Debian, use the following command: sudo apt install -y python3-venv Some Python packages require compilation from source code, so you might also need to install the following dependencies: sudo apt install -y build-essential libssl-dev libffi-dev python3-dev Now, let's see how to create a Python 3 virtual environment in Windows and Linux using venv. Step 1: Creating the Virtual Environment Use the following command for all operating systems: python -m venv venv Here, -m tells Python to run the venv module. The second venv specifies the directory (venv/lib/python3.8/site-packages/, the version may vary) where Python will store all libraries and components required for isolated application execution. Step 2: Activating the Virtual Environment Activation differs between Windows and Linux. On Windows, run: venv\Scripts\activate.bat On Linux (and MacOS), use: source venv/bin/activate If everything is set up correctly, you will see an output like this: (venv) root@purplegate:/var/test# Now you can start working on your project within the isolated environment! Other Tools Of course, venv is the most modern tool for creating virtual environments. However, it was only introduced in Python 3. So what should those working with older versions of Python do? The answer: try other tools that offer additional useful features—otherwise, we wouldn’t mention them at all. Below is a brief overview of these alternatives, followed by a more detailed look at the most popular one. virtualenv – A simple and user-friendly tool that is widely used when deploying applications. It’s useful to learn, and we’ll provide instructions on how to use it below. pyenv – Allows you to isolate different Python versions. This is helpful if you need to run multiple versions of Python, for example, for testing purposes. virtualenvwrapper – A wrapper for virtualenv that helps manage virtual environments by simplifying tasks like creating, copying, and deleting environments. One of its advantages is that it allows easy switching between environments and supports various plugins for extended functionality. Creating a Virtual Environment Using virtualenv Let's go through the process using Linux as an example. However, running Python virtualenv on Windows is almost the same, with differences mainly in file paths and scripts, which we’ll mention separately. Step 1: Install virtualenv You can download the source code and install it manually, but the easiest way is to use pip. Just enter the following command in your terminal: pip install virtualenv Step 2: Create a Virtual Environment This step requires just a simple command: virtualenv myenv This will create a new directory in the current folder. Instead of myenv, you can use any other name for your environment. Directory structure of the virtual environment: /myenv/bin – Contains scripts for managing the environment, a copy of the Python interpreter, pip, and some package management utilities. In Windows, this folder is located at /myenv/Scripts. /myenv/lib and /myenv/include – Store the environment’s core libraries. Any newly installed files will go into /myenv/lib/pythonX.X/site-packages/, where X.X represents your Python version. Step 3: Activate the Virtual Environment Activation differs slightly between Linux and Windows. For Linux, use: source myenv/bin/activate For Windows, run: myenv\Scripts\activate.bat Once activated, you will see the virtual environment’s name in your command line prompt. If you create the virtual environment with the --system-site-packages flag, it will have access to the system-wide package storage: virtualenv --system-site-packages myenv Keep in mind that the system package paths differ: Linux: /usr/lib/python3.8/site-packages Windows: \Python38\Lib\site-packages Version numbers may vary depending on your installation. Step 4: Deactivate the Virtual Environment Once you’re done working in the Python virtual environment, you should exit it properly. For Linux, run: deactivate For Windows, use the batch file: myenv\Scripts\deactivate.bat What's New? In addition to the venv and virtualenv modules we’ve already covered, there are more modern tools that provide greater flexibility in managing Python projects, including virtual environments: Poetry – A package manager that helps manage application dependencies within a virtual environment. It also simplifies testing and deployment by automating many processes. Pipenv – Another package manager that integrates pip and virtualenv, along with several other useful tools. It is designed to make environment and package management easier, as many developers eventually encounter version control issues in their projects. Each of these tools deserves a deep dive, but for now, let’s focus on the key features of both. Poetry: The Essentials Poetry handles all aspects of managing libraries within a virtual environment, including installing, updating, and publishing them. The functionality of pip alone is often insufficient for these tasks. Additionally, Poetry allows you to create and package applications with a single command (replace myproject with your actual project name): poetry new myproject If you want to initialize an existing directory as a Poetry project, use: poetry init Poetry can also: Publish projects to private repositories Track and manage dependencies Enforce version control Simplify working on private virtual servers by ensuring reliable project isolation Pipenv: The Essentials In simple terms, Pipenv is like pip + virtualenv, but with enhanced features. It eliminates the need for the traditional and sometimes cumbersome requirements.txt file. Instead, Pipenv uses: Pipfile.lock – Ensures package version consistency, which enhances security. Pipfile – A more advanced replacement for requirements.txt. Unlike its predecessor, Pipfile updates automatically as package versions change, which is particularly useful for teams, reducing dependency conflicts. Now you have a complete set of tools at your disposal, and managing multiple dependencies with different versions should no longer be a challenge!